Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Abraham, Jerin; Upreti, Kamal Title A name given to the resource Enhancing Lung Cancer Detection Accuracy: Implementing Smote for Balanced Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived 2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025;pp.138-143 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/IC363308.2025.10956812" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/IC363308.2025.10956812</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003905445?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003905445?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Abraham J., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India; Upreti K., Christ (Deemed to be University), Department of Computer Science, Ghaziabad, India Description An account of the resource This research goal is to forecast lung cancer using machine learning, and addressing the dataset's class imbalance is a top priority. The data that was initially gathered was extremely unbalanced, with 87.38% of instances being of the minority class of lung cancer and only 12.62% being non-cancer cases. To address this imbalance, minority over-sampling through self-generated SMOTE (Synthetic Minority Over-sampling Technique) was implemented wherein there were 64.85% cases of lung cancer and 35.15% of non-lung cancer cases after deduplication. Logistic regression (LR), Gaussian naive Bayes, Support Vector Machine (SVM), Bernoulli naive Bayes, K nearest neighbors (KNN), Random Forest (RF), multi-layer perceptron, and extreme gradient boosting are among the machine learning methods that were tested. The best test performance was shown by the Random Forest and Extreme Gradient Boosting methods that achieved an accuracy of 97.3% followed by K Nearest Neighbors at 95.95%, and Multi-Layer Perceptron at 93.24%. This highlights the necessity of data balance and the ways in which these methods can improve the efficacy of predictive models for lung cancer. As such, this addition contributes to the dearly needed critical knowledge which may be a stepping stone for innovation within the domains of diagnosis and treatment medicine through machine learning. 2025 IEEE. Subject The topic of the resource Class Imbalance; classification; Lung cancer Prediction; Machine Learning; SMOTE Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152749-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online